srikads/DataViz-MCP-Server

DataViz MCP Server

A comprehensive Model Context Protocol (MCP) server for intelligent data pattern analysis and visualization. This advanced analytics platform provides automated data discovery, pattern fingerprinting, natural language querying, and sophisticated visualization capabilities.

Architecture

The server follows an advanced modular architecture:

Auto-Discovery → Data Sources → Advanced Pattern Detection → AI Analysis → Pattern Fingerprinting → Similarity Engine → Natural Language Interface → Visualization Engine → Interactive Dashboard

Core Components

• Auto-Discovery Pipeline: Automatically discovers and evaluates data sources with confidence scoring
• Advanced Data Source Manager: Multi-format support (CSV, JSON, Excel, SQL, APIs, Streaming, Images)
• Advanced Pattern Detector: 15+ algorithms for comprehensive pattern analysis
• Pattern Fingerprinting System: Creates unique signatures for data patterns
• Pattern Similarity Engine: Finds and compares similar datasets using fingerprint matching
• Natural Language Processor: Ask questions about your data in plain English
• AI Analyzer: Intelligent insights, recommendations, and automated analysis
• Visualization Engine: Dynamic SVG generation optimized for different patterns
• MCP Tools: 25+ interactive tools accessible through the Model Context Protocol

Features

Data Sources

• ✅ File Formats: CSV, JSON, Excel with intelligent parsing
• ✅ Database Support: PostgreSQL, MySQL, SQLite with query optimization
• ✅ API Integration: REST APIs with caching and authentication
• ✅ Streaming Data: WebSocket and Kafka real-time data ingestion
• ✅ Image Processing: Extract data from charts, graphs, and tables using OCR/CV
• ✅ Web Scraping: Automated data extraction from websites
• ✅ Auto-Discovery: Intelligent scanning and evaluation of data sources

Advanced Pattern Detection

• ✅ Trend Analysis: Linear regression with confidence intervals
• ✅ Distribution Analysis: Normal, exponential, uniform, bimodal detection with goodness-of-fit
• ✅ Correlation Discovery: Pearson, Spearman, non-linear, polynomial relationships
• ✅ Clustering: K-means, hierarchical, DBSCAN with silhouette scoring
• ✅ Time Series: Seasonality, autocorrelation, autoregressive models (AR)
• ✅ Frequency Analysis: Peak detection, spectral entropy, dominant frequencies
• ✅ Anomaly Detection: Statistical outliers, change points, outlier clusters
• ✅ Cyclical Patterns: Multiple overlapping cycles with harmonic analysis

Visualizations

• ✅ Line charts
• ✅ Bar charts
• ✅ Scatter plots
• ✅ Histograms
• ✅ Box plots
• ✅ Heatmaps (correlation matrices)
• ✅ Pie charts

AI Analysis & Intelligence

• ✅ Pattern Fingerprinting: Unique signatures for datasets with statistical, temporal, relational components
• ✅ Similarity Matching: Find datasets with similar patterns using cosine similarity
• ✅ Natural Language Processing: Ask questions in plain English with intelligent query understanding
• ✅ Automated Insights: Context-aware recommendations and explanations
• ✅ Pattern Clustering: Group similar datasets automatically
• ✅ Statistical Significance: Confidence intervals, p-values, effect sizes for all patterns

Installation

1. Clone the repository:

git clone <repository-url>
cd DATAVIZ

2. Install dependencies:

npm install

3. Build the project:

npm run build

4. Start the interactive interface:

npm start

Quick Start Guide

🚀 Interactive Data Analysis (Recommended)

Start the interactive query interface to analyze data using natural language:

# Windows PowerShell
cd C:\Users\preci\DATAVIZ
node interactive-query-demo.js

# Linux/Mac Terminal
cd /path/to/DATAVIZ
node interactive-query-demo.js

Once started, you can ask questions like:

• what trends do you see in the sales data?
• show me correlations between revenue and sales
• find patterns in the user metrics
• create visualizations for seasonal patterns

📊 Generate Visualizations

Create actual SVG/HTML visualizations:

# Run visualization test
node test-visualization.js

# Check for generated files
ls *.svg *.html *.png

🦠 COVID-19 Data Analysis

Run comprehensive COVID-19 data analysis:

# Available COVID analysis commands
npm run demo           # COVID analysis demo
npm run test:covid     # Comprehensive COVID data test

📂 Analyze Your Own Data

1. Place your data files in the project directory:

   # Supported formats: CSV, JSON, Excel, TSV
   cp /path/to/your/data.csv ./
   cp /path/to/your/metrics.json ./
   

2. Start interactive analysis:

   node interactive-query-demo.js
   

3. Ask about your data:

   🤖 DataViz> analyze data.csv
   🤖 DataViz> what trends exist in my sales column?
   🤖 DataViz> create scatter plot of price vs demand
   🤖 DataViz> show correlations between all numeric columns
   

🎯 Available Commands

💡 Example Queries

Data Analysis Queries

🤖 DataViz> what seasonal trends exist in the data?
🤖 DataViz> find anomalies in the last 6 months
🤖 DataViz> show correlations between all variables
🤖 DataViz> what distribution patterns do you see?

Visualization Requests

🤖 DataViz> create time series plot for sales data
🤖 DataViz> generate scatter plot of x vs y
🤖 DataViz> show histogram of customer ages
🤖 DataViz> create correlation heatmap

COVID-19 Specific Queries

🤖 DataViz> what are seasonal trends in covid 19 confirmed cases?
🤖 DataViz> show correlations between deaths and confirmed cases
🤖 DataViz> find anomalies in covid death rates
🤖 DataViz> compare pattern similarity between countries

Development

Setup

# Install dependencies
npm install

# Run in development mode
npm run dev

# Run tests
npm test

# Lint code
npm run lint

Project Structure

src/
├── index.ts              # MCP server entry point
├── types/                # Type definitions
│   └── index.ts
├── services/             # Core business logic
│   ├── DataSourceManager.ts
│   ├── PatternDetector.ts
│   ├── AIAnalyzer.ts
│   └── VisualizationEngine.ts
├── tools/                # MCP tool definitions
│   └── index.ts
└── utils/                # Utility functions

MCP Tools

The server exposes 25+ tools through the Model Context Protocol:

Basic Data Management

• connect_data_source - Connect to CSV, JSON, or Excel files
• load_data - Load data from connected sources
• list_connections - View all active connections
• get_data_summary - Get overview of loaded data

Advanced Data Sources

• discover_data_sources - Auto-discover data sources with confidence scoring
• connect_advanced_source - Connect to SQL databases, APIs, streaming sources, images
• continuous_discovery - Monitor directories for new data sources
• get_streaming_status - Monitor real-time data connections
• manage_api_cache - Control API data caching

Pattern Analysis

• analyze_patterns - Basic pattern detection
• detect_advanced_patterns - Comprehensive analysis with 15+ algorithms
• generate_pattern_fingerprint - Create unique pattern signatures
• find_similar_patterns - Discover datasets with similar characteristics
• compare_datasets - Detailed comparison of two datasets
• cluster_similar_datasets - Group datasets by pattern similarity

Natural Language Interface

• ask_natural_language - Query data using plain English
• get_nl_examples - Get example questions you can ask

Visualization & Export

• generate_visualization - Create specific visualizations
• generate_dashboard - Create comprehensive dashboards
• export_analysis - Export results in JSON or HTML format
• export_fingerprints - Backup pattern fingerprints
• import_fingerprints - Restore fingerprint data

Usage Examples

Auto-Discovery Workflow

1. Discover Data Sources:

{
  "tool": "discover_data_sources",
  "args": {
    "directory": "/data",
    "recursive": true,
    "autoConnect": true,
    "maxConnections": 3
  }
}

2. Advanced Pattern Analysis:

{
  "tool": "detect_advanced_patterns",
  "args": {
    "connectionId": "auto_discovered_dataset"
  }
}

3. Generate Pattern Fingerprint:

{
  "tool": "generate_pattern_fingerprint",
  "args": {
    "connectionId": "auto_discovered_dataset"
  }
}

Natural Language Queries

Ask questions about your data in plain English:

{
  "tool": "ask_natural_language",
  "args": {
    "connectionId": "sales_data",
    "query": "What are the seasonal trends in our sales data?"
  }
}

{
  "tool": "ask_natural_language",
  "args": {
    "connectionId": "website_data", 
    "query": "Show me correlations between weather and website traffic"
  }
}

{
  "tool": "ask_natural_language",
  "args": {
    "connectionId": "user_data",
    "query": "Find anomalies in the last 6 months of user engagement"
  }
}

Advanced Data Sources

Connect to databases, APIs, and streaming data:

{
  "tool": "connect_advanced_source",
  "args": {
    "id": "postgres_db",
    "type": "sql",
    "config": {
      "connectionString": "postgresql://user:pass@localhost/db",
      "query": "SELECT * FROM sales_metrics WHERE date >= NOW() - INTERVAL '30 days'"
    }
  }
}

{
  "tool": "connect_advanced_source",
  "args": {
    "id": "api_metrics",
    "type": "api", 
    "config": {
      "apiUrl": "https://api.example.com/metrics",
      "apiKey": "your-api-key",
      "headers": {"Content-Type": "application/json"}
    }
  }
}

Pattern Similarity Analysis

Find datasets with similar characteristics:

{
  "tool": "find_similar_patterns",
  "args": {
    "connectionId": "target_dataset",
    "threshold": 0.8
  }
}

{
  "tool": "compare_datasets", 
  "args": {
    "connectionId1": "sales_q1", 
    "connectionId2": "sales_q2"
  }
}

Advanced Visualization

Create custom visualizations:

{
  "tool": "generate_visualization",
  "args": {
    "connectionId": "sales_data",
    "type": "scatter",
    "xAxis": "advertising_spend",
    "yAxis": "revenue",
    "colorBy": "region",
    "title": "Revenue vs Advertising Spend by Region"
  }
}

Pattern Detection Capabilities

The system automatically detects various data patterns:

Trends

• Linear trends using regression analysis
• Confidence scoring based on R-squared values
• Direction detection (increasing/decreasing)

Anomalies

• Statistical outlier detection (2+ standard deviations)
• Percentage of anomalous data points
• Data quality recommendations

Correlations

• Pearson correlation analysis
• Strong correlation identification (|r| > 0.7)
• Relationship strength assessment

Clusters

• K-means clustering with silhouette scoring
• Optimal cluster number detection
• Data segmentation insights

Seasonality

• Autocorrelation-based seasonal detection
• Period identification
• Amplitude measurement

Data Quality Assessment

The system provides comprehensive data quality analysis:

• Missing value detection
• Data type inference
• Statistical summaries
• Distribution analysis
• Recommendations for data improvement

Export Formats

Analysis results can be exported in multiple formats:

JSON Export

Complete structured data including:

• Detected patterns with confidence scores
• Statistical summaries
• Recommendations
• Visualization configurations

HTML Export

Human-readable reports featuring:

• Executive summary
• Pattern visualizations
• Statistical tables
• Actionable recommendations

Contributing

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Submit a pull request

License

MIT License - see LICENSE file for details

Roadmap

• Database connectivity (PostgreSQL, MySQL, SQLite)
• Real-time data streaming
• Machine learning model integration
• Advanced time series analysis
• Interactive web dashboard
• Collaborative analysis features
• Custom pattern detection algorithms
• Data transformation pipelines"# DataViz-MCP-Server"